1. Field of the Invention
The present invention relates to a face feature data compression device, in particular to the feature data compression device capable of compressing data by using the mirrorability and the transposability of the face feature data, and a multi-directional face detection system and a multi-directional face detection method applying the feature data compression device.
2. Description of the Related Art
In a conventional face detection method, testing pictures are inputted into a learning model, and the learning model will learn how to determine whether the testing pictures include any predetermined template feature. Regardless of an active learning structure such as a neural network, an expert system, a fuzzy system or a classified learning structure such as a support vector machine (SVM), a principal components analysis (PCA), a snow method or a boosting method, the learning can be achieved by a plurality of set template features.
To differentiate a face from a background in the testing pictures, a Haar-like algorithm is generally used for capturing the face features, wherein the Haar-like algorithm is an algorithm for processing the features based on a texture direction of patterns. Therefore, the Haar-like algorithm can differentiate a face from a complicated background effectively. Since the Haar-like algorithm relies on the texture direction of the testing pictures, therefore when the testing pictures are transposed to a different direction such as transposing the testing pictures with 90 degrees, 180 degrees or 270 degrees, the texture direction of the face in the testing picture will be different, so that the template feature computed by the original Haar-like algorithm cannot be used for the transposed testing pictures.
To detect the face of the testing pictures transposed to a different position, it is necessary to use the Haar-like algorithm to perform a learning training of the face features of the testing pictures at different transposed positions to generate corresponding template features of the different transposed positions. Obviously, the aforementioned method not only wastes a large quantity of memory spaces, but also wastes several times of the computing time.
However, a present mobile device such as a camera, a mobile phone or a camcorder generally comes with a light, thin, short and compact design for convenient carry, therefore the mobile device usually provides limited memory spaces. To reduce the data volume of the template features, the angle of the face detection is reduced. For example, a planar rotation angle or a side face angle is used for the detection or a lower precision of the template features is adopted. However, these methods generally result in a low face detection performance due to the factors of limited angle, low brightness or dark skin color.